用社交媒体文本检测双相情感障碍，上下文嵌入比静态词向量更关键。
Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
- 对比了多种模型在上下文嵌入下的表现，发现RoBERTa效果最佳。
- 使用BERT嵌入的LSTM模型达到近98%的F1分数，而静态词向量几乎无效。
- 适合心理健康与NLP交叉研究者参考，尤其关注早期筛查应用。
双相情感障碍是一种常因早期症状隐蔽和社 会偏见而未被诊断的慢性精神疾病。本文探讨基于用户生成的社交媒体文本，利用先进自然语言处理模型识别双相情感障碍迹象。我们对基于Transformer的模型（BERT、RoBERTa、ALBERT、ELECTRA、DistilBERT）以及基于上下文嵌入（BERT）和静态嵌入（GloVe、Word2Vec）的LSTM模型进行了全面评估。实验在经过情感波动分析与判断验证的大型Reddit帖子数据集上进行。结果表明，RoBERTa在各类Transformer模型中表现最优，F1分数达约98%；而使用BERT嵌入的LSTM模型也取得相近结果。相反，仅依赖静态嵌入的LSTM模型无法捕捉有效模式，F1接近零。这凸显了上下文建模在识别双相障碍中的核心作用。此外，我们报告了训练时间，指出DistilBERT在效率与准确率间实现最佳平衡。本研究为心理健康NLP应用中的模型选择提供了可操作洞见，并验证了上下文化语言模型在早期双相障碍筛查中的潜力。
原文摘要 · Abstract (English)
Bipolar disorder is a chronic mental illness frequently underdiagnosed due to subtle early symptoms and social stigma. This paper explores the advanced natural language processing (NLP) models for recognizing signs of bipolar disorder based on user-generated social media text. We conduct a comprehensive evaluation of transformer-based models (BERT, RoBERTa, ALBERT, ELECTRA, DistilBERT) and Long Short Term Memory (LSTM) models based on contextualized (BERT) and static (GloVe, Word2Vec) word embeddings. Experiments were performed on a large, annotated dataset of Reddit posts after confirming their validity through sentiment variance and judgmental analysis. Our results demonstrate that RoBERTa achieves the highest performance among transformer models with an F1 score of ~98% while LSTM models using BERT embeddings yield nearly identical results. In contrast, LSTMs trained on static embeddings fail to capture meaningful patterns, scoring near-zero F1. These findings underscore the critical role of contextual language modeling in detecting bipolar disorder. In addition, we report model training times and highlight that DistilBERT offers an optimal balance between efficiency and accuracy. In general, our study offers actionable insights for model selection in mental health NLP applications and validates the potential of contextualized language models to support early bipolar disorder screening.
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